International Congress on Abnominal Obesity
Am J Clin Nutr 89: 558-567, 2009. First published January 13, 2009; doi:10.3945/ajcn.2008.26720
 American Journal of Clinical Nutrition, doi:10.3945/ajcn.2008.26720
Vol. 89, No. 2, 558-567, February 2009
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© 2009 American Society for Clinical Nutrition

ORIGINAL RESEARCH COMMUNICATION

Prolonged saturated fat–induced, glucose-dependent insulinotropic polypeptide elevation is associated with adipokine imbalance and liver injury in nonalcoholic steatohepatitis: dysregulated enteroadipocyte axis as a novel feature of fatty liver

Giovanni Musso1,2,3, Roberto Gambino1,2,3, Giovanni Pacini1,2,3, Franco De Michieli1,2,3 and Maurizio Cassader1,2,3

1 From the Gradenigo Hospital, Turin, Italy (GM); the Department of Internal Medicine, University of Turin, Turin, Italy (RG, FDM, and MC); and the Metabolic Unit, Institute of Biomedical Engineering, National Research Council, Padua, Italy (GP).

2 Supported by the Piedmont Region Funds Comitato Interministeriale per la Programmazione Economica 2008.

3 Reprints not available. Address correspondece to G Musso, Gradenigo Hospital, C.so Regina Margherita 8, 10132 Torino, Italy. E-mail: giovanni_musso{at}yahoo.it.


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ABSTRACT
 
Background: Genetic and acquired mechanisms underlying the association of nonalcoholic fatty liver disease (NAFLD) with diabetes are unknown. Glucose-dependent insulinotropic polypeptide (GIP) was recently linked to adipocyte metabolism and obesity-related metabolic disorders, including NAFLD, induced by an excess of saturated fatty acids (SFAs), but its role in vivo, as well as underlying mechanisms, is unknown. We hypothesized that altered GIP secretion may contribute to the pathogenesis of NAFLD.

Objective: We assessed GIP response to SFA ingestion and its effect on glucose and lipid metabolism and on liver injury in patients with nonalcoholic steatohepatitis (NASH).

Design: Thirty-two nonobese, nondiabetic patients with NASH and 32 healthy controls matched for age, body mass index, and sex underwent a 7-d dietary record, an oral-glucose-tolerance test (OGTT), and a high–fat-load test. OGTT-derived indexes of glucose homeostasis were calculated; circulating lipoproteins, total antioxidant status, GIP, adiponectin, resistin, and cytokeratin-18 fragments (markers of hepatocyte apoptosis) after a high-fat meal were assessed. All subjects were genotyped for transcription factor 7–like 2 (TCF7L2) polymorphism.

Results: Patients with NASH exhibited a prolonged GIP elevation after fat ingestion. GIP response correlated directly with hepatic steatosis, postprandial resistin, and free fatty acid (FFA) increase and inversely with β cell function and incretin effect. Dietary polyunsaturated:saturated fatty acid ratio and TCF7L2 polymorphism independently predicted postprandial GIP response. Cytokeratin-18 fragments increased significantly postprandially in both groups but more consistently in patients with NASH; their increase was predicted by postprandial adiponectin and FFA responses.

Conclusions: GIP response to SFA ingestion is prolonged in nondiabetic patients with NASH and is correlated with liver disease, an unfavorable dynamic adipokine profile, and β cell dysfunction, which provides a rationale for GIP antagonism in these subjects.


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INTRODUCTION
 
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease and a risk factor for diabetes, independently of insulin resistance and other classic risk factors (1, 2). Incretin mimetics are being currently investigated for the treatment of diabetes and were able to reverse hepatic steatosis in small reports, but the efficacy and safety profile of different strategies that enhance incretin action, ie, glucagon-like peptide 1 (GLP-1) analogs, glucose-dependent insulinotropic polypeptide (GIP) agonists, or dipeptidyl peptidase 4 inhibitors, are unclear (3).

GIP is an emerging key modulator of lipid metabolism: acute and chronic administration of GIP, but not of GLP-1, induced fatty liver and other obesity-associated metabolic disorders in animal models and stimulated resistin secretion in cultured adipocytes (4, 5). Consistently, GIP antagonism reversed liver, muscle, and adipose tissue triglyceride infiltration and high-fat-induced metabolic disturbances (6, 7). Altogether these data suggest GIP may mediate the deleterious metabolic effects of a high-fat diet and may modulate adipokine secretion, independently of its incretin effect on β cell function (8). The possible role of GIP in the pathogenesis of fatty liver, as well as its associations with adipokine secretion in vivo, has not been studied.

Factors modulating GIP secretion are unclear: among dietary factors, fat is the most potent stimulator of GIP secretion, with different types of fat exerting different stimulatory effects (9, 10). High saturated fatty acid (SFA) feeding is also an established experimental model of nonalcoholic steatohepatitis (NASH), the progressive form of NAFLD, and excessive SFA intake predisposes to diabetes, but mechanisms underlying this association are unclear (11–14).

Among genetic factors, rs7903146C/T polymorphism in transcription factor 7–like 2 (TCF7L2) was associated in the general population with impaired secretion of the other incretin GLP-1 and with an increased risk of diabetes and in NAFLD with the severity of liver disease, but its effect on GIP secretion is unknown (15, 16).

We assessed acute GIP response to fat ingestion and its relation to liver disease, adipokine secretion, and glucose homeostasis and lipoprotein metabolism in patients with NASH. Furthermore, we evaluated the genetic and dietary determinants of the postprandial GIP response.

The presence of obesity and diabetes was a criterion for exclusion, because we aimed at identifying early mechanisms predisposing to future development of metabolic disease, and different adipokines may intervene as metabolic disease progresses to diabetes, dyslipidemia, and obesity. Furthermore, obesity and diabetes are per se independently associated with altered β cell function and GIP secretion or action (17, 18). Postprandial lipoprotein metabolism was assessed; because GIP exerts its action postprandially, postprandial lipemia is an established cardiovascular risk factor and contributes substantially to liver triglyceride accumulation in NAFLD (19).


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SUBJECTS AND METHODS
 
Subject selection
Among 69 patients referred by family physicians to our Hepato-Metabolic Clinic for persistent liver enzyme elevations in 2007, 32 patients with biopsy-proven NASH were identified (20). In addition to histologic evidence, the diagnosis of NASH required all of the following criteria: ultrasonographic absence of any other liver or biliary disease and a history of alcohol consumption <20 g/d in men and <10 g/d in women, as assessed by a detailed interview extended to family members and by a 1-wk questionnaire.

Exclusion criteria were as follows: exposure to occupational hepatotoxins or drugs known to be steatogenic or hepatotoxic or to affect lipid or glucose metabolism; autoimmune or celiac disease; positive viral markers; abnormal copper metabolism, serum {alpha}1-antitripsin, or thyroid function; and obesity, diabetes, or dyslipidemia. Mutations in the hemochromatosis genes HFE and TRF2 were detected in patients and controls with the use of multiplex amplification reaction (Nuclear Laser Medicine, Milan, Italy).

The control group consisted of 32 healthy subjects comparable for age, sex, body mass index (BMI; in kg/m2), and waist circumference. In addition to a negligible alcohol intake (<20 g/d in men and <10 g/d in women) and normal abdominal ultrasound scan, the upper healthy alanine transaminase limit was lowered to <30 U/L (men) and <20 U/L (women) to rule out subclinical liver disease (21, 22). Patients and controls gave their consent to the study, which was conducted according to Declaration of Helsinki ethical principles.

Genetic analyses
Patients and controls were genotyped for TCF7L2 rs7903146 C/T polymorphism by the real-time TaqMan Allelic Discrimination Assay (Applied Biosystems, Foster City, CA). Apolipoprotein E polymorphism was determined by polymerase chain reaction amplification of genomic DNA.

Dietary record
Subjects completed a daily dietary record for 1 wk, according to the protocol of the European Prospective Investigation into Cancer, which was analyzed with the use of the WINFOOD database (Medimatica, Teramo, Italy) as previously described (23, 24).

Endothelial dysfunction
Soluble adhesion molecules E-selectin, intercellular adhesion molecule-1, and vascular adhesion molecule-1 were measured by a solid-phase enzyme-linked immunoabsorbent assay (ELISA; R&D Systems, Minneapolis, MN). Intra- and interassay CVs were, respectively, 4.7%–5.0% and 7.4%–8.8%, 2.3%–3.6% and 5.5%–7.8%, and 4.7%–5.0% and 7.4%–8.8%.

Cytokines
Serum resistin and adiponectin were measured by immunoenzymatic methods. Serum adiponectin was measured by sandwich ELISA, with intra- and interassay CVs of 3.4% and 5.8% (R&D Systems Europe Ltd, Abingdon, United Kingdom). Serum resistin was measured by an enzyme immunoassay (Bio Vendor Laboratory Medicine Inc, Brno, Czech Republic). The intra- and interassay CVs were, respectively, 2.8%–3.4% and 5.5%–6.8%.

Oral fat load
Study subjects underwent an oral-fat-load test, according to a protocol previously used by our group (25). To prevent any interference of postprandial glucose and insulin elevation on adipokine secretion and free fatty acid (FFA) metabolism, the meal was virtually carbohydrate free (25). Samples were drawn every 2 h for 10 h, and plasma total cholesterol, triglycerides, FFAs, resistin, adiponectin, GIP, glucose, and insulin were measured at each time. Triglyceride-rich lipoproteins (TRLPs) were isolated through preparative ultracentrifugation and subfractionated, as previously described (26). Apolipoprotein B-48 (apo B-48) and apolipoprotein B-100 (apo B-100) content of TRLP subfractions were quantified by sodium dodecyl sulfate–polyacrylamide gel electrophoresis.

Circulating GIP
Serum total human GIP was measured by sandwich ELISA (Linco, St Charles, MO). The kit has a sensitivity of 8.2 pg/mL in a 20-µL sample size and a range of 8.2–2000 pg/mL. The intra- and interassay CVs were 3.0% and 2.3%, respectively.

Total antioxidant status
Measurement of plasma total antioxidant status (TAS) in the fat load test samples is based on the reduction of Cu2+ into Cu+ by the action of all present antioxidants. The amount of Cu+ is assessed through measuring the complex formed by Cu+ and bathocuproine. This complex has a typical absorption at 490 nm (ANTOXT kit; Fujirebio Diagnostics AB, Göteborg, Sweden) (27).

Markers of hepatocyte apoptosis
To assess whether fat ingestion acutely induces hepatocyte apoptosis, plasma hepatic caspase-3–generated cytokeratin-18 (CK-18) fragments were measured with the use of the M30-Apoptosense ELISA kit. The M30-Apoptosense ELISA kit, a one-step in vitro immunoassay for the quantitative determination of the apoptosis-associated CK18Asp396 neo-epitope in serum (PEVIVA AB, Bromma, Sweden), has a sensitivity of 25 U/L in a 25-µL sample size and a range of 75–1000 U/L. The intra- and interassay CVs are <8% (28–30).

Oral-glucose-tolerance test minimal model indexes of glucose homeostasis
After completion of the alimentary record, patients and controls underwent a standard 75-g oral-glucose-tolerance test (OGTT), and indexes of glucose homeostasis were calculated. Areas under the concentration curves (AUCs) of glucose, insulin, and C-peptide during the OGTT were calculated with the trapezoidal method. Prehepatic insulin delivery was estimated as the suprabasal ({Delta}) 30-min AUC of C-peptide divided by the 30-min increase in plasma glucose. Two indexes of insulin sensitivity were calculated: the conventional quantitative insulin-sensitivity check index and oral glucose insulin sensitivity (OGIS), an OGTT-derived index of whole-body insulin sensitivity (31, 32). The hepatic insulin extraction, as a percentage of secreted hormone, was estimated by [1 – (AUC insulin/AUC C-peptide)].

Two OGTT-derived indexes of β cell function, the insulinogenic index (IGI), computed as the suprabasal serum insulin increment divided by the corresponding plasma glucose increment in the first 30 min ({Delta}I30/{Delta}G30), and the C-peptide–genic index, computed as {Delta}C-peptide30/{Delta}G30, which were previously validated against measures of β cell functions derived from the frequently sampled intravenous glucose tolerance test (FIVGTT), were calculated (33, 34).

The ability of β cells to adapt insulin secretion to changes in insulin sensitivity was assessed by 2 indexes, the disposition index and the adaptation index, which are calculated by multiplying OGIS x IGI and C-peptide–genic index values, respectively. These indexes relate β cell insulin secretion to insulin resistance and represent integrated parameters of β cell function, validated against FIVGTT minimal model variables in nondiabetic subjects (35); they also accurately predict future type 2 diabetes in the general population (36).

Incretin effect
To assess whether differences in β cell function were related to a reduced incretin stimulatory effect on β cells, the incretin effect was assessed. Patients and controls underwent an "isoglycemic" intravenous glucose infusion during an FIVGTT (35). Incretin effect was calculated by relating the differences in β cell responses between stimulation with oral and intravenous glucose to the response after oral glucose, which was taken as 100%. The following formula was used: 100% x (AUCinsulin OGTT – AUCinsulin FIVGTT)/AUCinsulin OGTT (17).

Statistical analysis
Differences between groups were analyzed by analysis of variance (ANOVA) for normal variables or the Mann-Whitney test for other variables. Normality was evaluated by the Shapiro-Wilk test. The Fisher or chi-square test was used to compare categorical variables, as appropriate. Data were expressed as mean ± SEM. Differences were considered statistically significant at P < 0.05.

The AUC and incremental AUC of different variables during the oral fat test and glucose tolerance tests were computed by the trapezoidal method. Multivariate repeated-measures ANOVA was used to test the interaction between time and group during the oral fat load test. When a significant interaction was found between factors, differences across groups were analyzed by ANOVA followed by Bonferroni's correction if variables were normally distributed; otherwise, the Mann-Whitney U test was performed, followed by the post hoc Dunn test to compare nonparametric variables.

Univariate correlations of dietary, anthropometric, and metabolic variables and of genetic polymorphisms were analyzed by Spearman's correlation test. Polymorphisms were modeled as an additive effect, ie, quantitative predictor variables reflecting the number of risk alleles (0, 1, or 2) as defined previously (15). Multiple regression analysis was applied when multiple associations were detected on univariate analysis. A logistic regression model was also used to identify independent predictors for severe (>66% hepatocytes) steatosis, grade 3 necroinflammation, or stage 3 fibrosis. The covariates were as follows: age; BMI; waist circumference; OGIS; fasting and postprandial adiponectin, resistin, and CK-18 fragments; and incremental AUC GIP, triglyceride, FFA, and VLDL1–apoB-48 and VLDL1–apoB-100 (STATISTICA software, version 5.1; Statsoft Italia, Padua, Italy).


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RESULTS
 
Subject characteristics and dietary record
The characteristics and dietary habits of patients and controls are represented in Table 1 and Table 2, respectively.


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  		TABLE 1. Baseline characteristics of patients with nonalcoholic steatohepatitis (NASH) and controls1


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  		TABLE 2. Daily intake of main dietary constituents in patients with nonalcoholic steatohepatitis (NASH) and controls1

Oral fat tolerance test
Postprandial plasma triglycerides, FFAs, and VLDL subfraction responses of both intestinal and hepatic origin were higher in patients with NASH than in controls (Figure 1; Table 3). LDL cholesterol did not change throughout the test (not shown). Despite comparable fasting values, plasma total antioxidant status rose in controls, whereas it fell postprandially in patients with NASH (Figure 1; Table 3). Fasting and postprandial plasma CK-18 fragments were higher in patients with NASH than in controls and increased significantly postprandially within each group (Table 3; Figure 2). Despite comparable fasting values, plasma GIP and resistin increased significantly postprandially, and the increase was higher in patients with NASH than in controls (Figure 3; Table 3). Fasting plasma adiponectin was lower in patients than in controls and significantly rose postprandially in controls, whereas it fell smoothly in patients with NASH (Figure 3; Table 3). Glucose and insulin concentrations did not significantly change throughout the test (not shown).


Figure 1
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  		FIGURE 1. Postprandial plasma triglycerides (A), free fatty acids (FFAs) (B), and total antioxidant status (TAS) (C) during the fat-load test in patients with nonalcoholic steatohepatitis (NASH) and controls. Data are presented as means ± SEMs. The area under the curve (AUC) and the incremental AUC of lipid variables, glucose-dependent insulinotropic polypeptide, adiponectin, and resistin were computed by the trapezoidal method. Multivariate repeated-measures ANOVA was used to test the interaction between time and group during the oral fat load test. When a significant interaction was found between factors, differences across groups were analyzed by ANOVA followed by Bonferroni's correction if variables were normally distributed; otherwise, the Mann-Whitney U test, followed by post hoc Dunn's test, was used. Time-by-group interaction was significant for triglycerides, FFAs, and TAS. *Significantly different from basal values (P < 0.05); {dagger}significantly different from controls (P < 0.05); {ddagger}significantly different from controls (P < 0.01).


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  		TABLE 3. Oral fat load variables of patients with nonalcoholic steatohepatitis (NASH) and controls1


Figure 2
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  		FIGURE 2. Postprandial plasma cytokeratin-18 (CK-18) fragments after the high-fat load in patients with nonalcoholic steatohepatitis (NASH) and controls. Data are presented as means ± SEMs. The area under the curve (AUC) and the incremental AUC of lipid variables, glucose-dependent insulinotropic polypeptide, adiponectin, and resistin were computed by the trapezoidal method. Multivariate repeated-measures ANOVA was used to test the interaction between time and group during the oral fat load test. When a significant interaction was found between factors, differences across groups were analyzed by ANOVA followed by Bonferroni's correction if variables were normally distributed; otherwise, the Mann-Whitney U test, followed by post hoc Dunn's test, was used. Time-by-group interaction was significant. *Significantly different from basal values (P < 0.05); {ddagger}significantly different from controls (P < 0.01).


Figure 3
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  		FIGURE 3. Postprandial plasma glucose-dependent insulinotropic polypeptide (GIP) (A), resistin (B), and adiponectin (C) after the high-fat load in patients with nonalcoholic steatohepatitis (NASH) and controls. Data are presented as means ± SEMs. The area under the curve (AUC) and incremental AUC of lipid variables, GIP, adiponectin, and resistin were computed by the trapezoidal method. Multivariate repeated-measures ANOVA was used to test the interaction between time and group during the oral fat load test. When a significant interaction was found between factors, differences across groups were analyzed by ANOVA followed by Bonferroni's correction if variables were normally distributed; otherwise, the Mann-Whitney U test, followed by post hoc Dunn's test, was used. Time-by-group interaction for GIP, resistin, and adiponectin was significant. *Significantly different from basal values (P < 0.05); {dagger}significantly different from controls (P < 0.05); {ddagger}significantly different from controls (P < 0.01).

OGTT-derived indexes of glucose homeostasis
OGTT-derived indexes of glucose homeostasis are represented in Table 4. β Cell function, as estimated by the variables IGI, C-peptide index, disposition index, and adaptation index, and incretin effect were reduced in patients with NASH, who were also more insulin resistant than were the controls.


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  		TABLE 4. Oral-glucose-tolerance test (OGTT)–derived indexes of glucose homeostasis in patients with nonalcoholic steatohepatitis (NASH) and controls1

Correlative analysis
Main univariate correlations between anthropometric, metabolic, and dietary variables in patients with NASH are shown in Table 5. The main determinants of metabolic and histologic variables on multiple and logistic regression analysis are shown in Table 6 and Table 7.


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  		TABLE 5. Multiple regression analysis of variables found to be significantly associated on univariate analysis in patients with nonalcoholic steatohepatitis (n = 32)1


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  		TABLE 6. Determinants of severe liver disease in patients with nonalcoholic steatohepatitis (NASH) on logistic regression analysis1


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  		TABLE 7. Main Spearman correlation coefficients between different variables in patients with nonalcoholic steatohepatitis (NASH)1


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DISCUSSION
 
The main findings of our study are as follows:

1) GIP response to SFA ingestion is prolonged in patients with NASH than in healthy controls.
2) Prolonged GIP response correlates with the severity of liver disease, with postprandial adipokine and FFA responses, and with reduced β cell function and the incretin effect in patients with NASH.
3) rs7903146C/T TCF7L2 polymorphism and the type of fat in the background diet modulate postprandial GIP response. GIP is an emerging modulator of postprandial nutrient homeostasis. In addition to the pancreatic β cell, GIP receptor is expressed on adipose tissue and intestine, where this incretin exerts largely unknown actions.

The potent and prolonged stimulation of GIP secretion by different types of fat suggests that this hormone modulates fat metabolism (9, 37). Recent data have shown the key role of GIP in the pathogenesis of obesity: acute and chronic administration of GIP, but not of GLP-1, induced fatty liver and other obesity-associated metabolic disorders in mice, whereas GIP antagonists reversed adipose tissue, liver, and muscle triglyceride accumulation and improved associated metabolic disturbances induced by a high-fat diet (5, 6). GIP administration consistently stimulates glucose uptake, lipoprotein lipase activity, and FFA synthesis and incorporation in adipocytes (8); furthermore, GIP also modulates adipokine secretion, thus constituting an enteroadipocyte axis (4, 5). In diabetic subjects, GIP response was exaggerated after a mixed meal but not after glucose ingestion, whereas GLP-1 concentrations were unaffected by meal composition, suggesting an increased GIP response to nutrients other than glucose may predispose to glucose intolerance (38). We found a prolonged GIP response to a high-SFA meal in nonobese nondiabetic patients with NASH. Although causality cannot be inferred from our cross-sectional study, potential mechanisms that link postprandial GIP elevation to metabolic derangement include postprandial resistin and FFA elevations, the latter possibly mediated by lipoprotein lipase–mediated lipolysis of circulating TRLPs (5). Consistently, GIP agonists increased plasma resistin concentrations in animal models; acute and chronic fat feeding increased plasma resistin and leptin concentrations in animals, an effect totally prevented by GIP antagonism.

Finally, coincubation of 3T3-L1 adipocytes with GIP, but not with GLP-1, resulted in a 3-fold increase in resistin mRNA expression and secretion; the latter becoming significant after 2-h incubation with GIP, a temporal pattern that is consistent with the 2-h latency in plasma resistin increase seen in our patients (4, 5). Intriguingly, resistin was required for GIP-induced lipoprotein lipase activation, suggesting that late resistin elevation may contribute to the prolonged postprandial nephremia of our patients. These findings suggest that GIP may be an important mediator of the adipocyte response to dietary SFA intake, through molecular mechanisms still largely undefined.

GIP response was also associated with a reduced β cell function and incretin effect in the patients with NASH. These findings suggest prolonged GIP elevation impairs β cell sensitivity to incretin stimulation, possibly by down-regulating β cell GIP receptors, and provide a basis for the early β cell GIP resistance in subjects at increased risk of diabetes (37, 39). Therapeutically, as incretin analogs are evaluated in NAFLD (3), the relative merits of inhibition compared with activation of GIP signaling should be weighed. Although GLP-1 agonists may be potentially effective and harmless, the benefits of GIP antagonism may overcome ablation of the insulin-releasing GIP component of the enteroinsular axis, already failing in these subjects, and represent a more suitable strategy for the treatment of NAFLD and obesity-related disorders (40). This is consistent with recent data comparing the effects of the administration of GLP-1 agonists, GIP agonists, or dipeptidyl peptidase-4 inhibitors on β cell function, insulin sensitivity, body weight, and adipokine concentrations in high-fat-fed mice: GIP agonists led in fact to insulin resistance and increased plasma leptin and resistin concentrations (41).

The determinants of postprandial GIP response in our subjects are the type of dietary fat and the TCF7L2 polymorphism. Experimental data confirmed that the type of dietary fat may modulate GIP secretion: high-SFA intake, as seen in our patients, promoted K-cell hyperplasia, enhanced GIP secretion, and led to obesity and NAFLD in mice, whereas GIP receptor–deficient animals were protected (42, 43). The type of dietary fat may also directly modulate chronic and postprandial adiponectin secretion from adipocytes, because it was shown in animal and human models (44–46). Future studies are needed to assess the effect of different types of fat on GIP and adipokine secretion and on liver disease.

TCF7L2 polymorphism has been linked to diabetes through impaired insulin secretion, an effect thought to be mediated by reduced GLP-1 secretion (15). The novel association of at-risk TCF7L2 polymorphism with a prolonged GIP elevation in response to fat ingestion provides a further basis for the diabetogenic effects of this gene, whose mechanisms are under investigation. Consistent with recent findings from our group (16), the interaction of postprandial lipid metabolism with GIP and adipokines induced an acute elevation in circulating markers of hepatocyte apoptosis in both patients and controls. The postprandial increase in circulating CK-18 fragments links SFA ingestion to liver injury and necroinflammation. CK-18 is a major cytoplasmatic filament protein in hepatocytes and is cleaved by caspase-3 during apoptosis. Circulating and intrahepatic CK-18 fragment concentrations are highly interrelated and accurately predict the severity of hepatocyte apoptosis and of histologic necroinflammation in human NASH (28–30). Although they are elevated in severe intrahepatic cholestasis, cholangitis, or hepatocellular carcinoma, their specificity for hepatocyte necroinflammation appears relatively high once these conditions are excluded (30). CK-18 fragments increased significantly, although to a lesser extent in healthy controls, suggesting that SFA ingestion is per se able to trigger hepatocyte apoptosis, which may be countered by protective mechanisms (ie, adiponectin increase) in healthy subjects, thus limiting the intrahepatic necroinflammatory process. The association of FFAs with circulating markers of hepatocyte apoptosis is consistent with experimental data: FFAs, and more so SFAs rather than polyunsaturated fatty acids, triggered c-Jun NH2-terminal kinase 1–mediated hepatocyte apoptosis, independently of cytokine action in cultured hepatocytes (47). Furthermore, FFAs activate the proinflammatory transcription factor nuclear factor-{kappa}B in hepatocytes, which leads to hepatic necroinflammation (48). Finally, FFAs promote endoplasmic reticulum stress and the unfolding protein response, a stress response to various stimuli that was recently linked to the pathogenesis of liver injury in NASH (49). Although the cross-sectional nature of our study prevents any definitive causal inference, the association of the postprandial phase with post-load CK-18 fragment elevation in both patients with NASH and healthy subjects suggests postprandial nephemia may per se injure the liver and could be an attractive therapeutic target in NASH, even in normolipidemic subjects.

In conclusion, an increased GIP response to SFA consumption characterizes NASH even in the absence of obesity and diabetes and is associated with the severity of liver disease and with an unfavorable metabolic profile. The benefit of GIP antagonism in NAFLD and the constellation of metabolic disorders need to be prospectively confirmed in large cohort studies. We used an SFA-rich meal because a high-SFA meal was associated with metabolic complications, and SFAs induced apoptosis and liver injury in cultured hepatocytes and animal models of NASH (10, 14, 44–46). Future studies need to explore the effect of different types of fat on the enteroadipocyte axis and liver injury in NASH, an issue with potentially relevant therapeutic implications. Finally, studies will need to clarify whether these fat-induced changes are specific for NASH or whether they characterize other types of liver disease as well. The fact that SFA ingestion enhances hepatocyte apoptosis and adipokine imbalance in healthy subjects as well suggests these mechanisms are not specific for fatty liver and opens potentially relevant therapeutic issues in different metabolic disorders. Limitations of this study are its cross-sectional nature, which prevents any causal inference, and the small number of subjects. Furthermore, because a normal liver ultrasound scan is an insensitive method to detect mild steatosis, some controls might have had NAFLD, despite stricter normal cutoff alanine transaminase values; even so, misclassification of NAFLD would attenuate the magnitude of the between-group difference in GIP and adipokine responses, leading to underestimation of the difference between NASH and health.


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ACKNOWLEDGMENTS
 
We thank Natalina Alemanno and Barbara Uberti for their help in laboratory analyses.

The authors' responsibilities were as follows—GM: study design, data elaboration, statistical analysis, discussion, and manuscript writing; RG: laboratory analyses and data elaboration; GP: modeling analysis of parameters of glucose metabolism; FDM: dietary data collection and elaboration; MC: data analysis and critical review of the manuscript. None of the authors had a conflict of interest.


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Received for publication July 21, 2008. Accepted for publication November 5, 2008.





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